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dc.contributor.authorWilcox, Ethan
dc.contributor.authorLevy, Roger
dc.contributor.authorMorita, Takashi
dc.contributor.authorFutrell, Richard
dc.date.accessioned2021-11-03T14:38:32Z
dc.date.available2021-11-03T14:38:32Z
dc.date.issued2018
dc.identifier.urihttps://hdl.handle.net/1721.1/137202
dc.language.isoen
dc.publisherAssociation for Computational Linguisticsen_US
dc.relation.isversionof10.18653/v1/w18-5423en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceAssociation for Computational Linguisticsen_US
dc.titleWhat do RNN Language Models Learn about Filler–Gap Dependencies?en_US
dc.typeArticleen_US
dc.identifier.citationWilcox, Ethan, Levy, Roger, Morita, Takashi and Futrell, Richard. 2018. "What do RNN Language Models Learn about Filler–Gap Dependencies?." Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences
dc.contributor.departmentMassachusetts Institute of Technology. Department of Linguistics and Philosophy
dc.relation.journalProceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLPen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-04-06T18:16:25Z
dspace.orderedauthorsWilcox, E; Levy, R; Morita, T; Futrell, Ren_US
dspace.date.submission2021-04-06T18:16:26Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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